Design and Development of a Hybrid Eye and Mobile Controlled Wheelchair Prototype using Haar cascade Classifier: A Proof of Concept


According to the wheelchair foundation, about 1.86% of the world’s population requires a functional wheelchair. Most of these wheelchairs have manual control systems which puts millions of people with total paralyzes (total loss of muscle control including the head) at a disadvantage. However, the majority of those who suffer from muscular and neurological disorders still retain the ability to move their eyes. Hence the concept of eye-controlled wheelchair. This paper focused on the design and development of a hybrid control system (eye and mobile interface) for a wheelchair prototype as a proof of concept. The systemwas implemented using the pre-trained Haar cascade ML classifier in open CV. Focus was shifted from high accuracy common to lab-based studies to deployment and power consumption which are critical to usability. The system consists of a motor chassis that takes the place of a wheelchair, a raspberry pi4 module which acts as a mini-computer for image and information processing, and a laser sensor to achieve obstacle avoidance. The Bluetooth module enables serial communication between the motor chassis and the raspberry pi, while the power supply feeds the raspberry pi and the camera. The system performance evaluation was carried out using obstacle avoidance and navigation tests. An accuracy of 100% and 89% were achieved for obstacle avoidance and navigation, respectively, which shows that the system would be helpful for wheelchair users facing autonomous mobility issues.



Paralysis · Wheelchair · Eye control · Obstacles avoidance · Camera